StarCraft: Brood War remains a challenging benchmark for artificial intelligence research, particularly in the domain of macromanagement, where long-term strategic planning is required. Traditional approaches to StarCraft AI rely on rule-based systems or supervised deep learning, both of which face limitations in adaptability and computational efficiency. In this work, we introduce NeuroPAL, a neuroevolutionary framework that integrates Neuroevolution of Augmenting Topologies (NEAT) with Punctuated Anytime Learning (PAL) to improve the efficiency of evolutionary training. By alternating between frequent, low-fidelity training and periodic, high-fidelity evaluations, PAL enhances the sample efficiency of NEAT, enabling agents to discover effective strategies in fewer training iterations. We evaluate NeuroPAL in a fixed-map, single-race scenario in StarCraft: Brood War and compare its performance to standard NEAT-based training. Our results show that PAL significantly accelerates the learning process, allowing the agent to reach competitive levels of play in approximately half the training time required by NEAT alone. Additionally, the evolved agents exhibit emergent behaviors such as proxy barracks placement and defensive building optimization, strategies commonly used by expert human players. These findings suggest that structured evaluation mechanisms like PAL can enhance the scalability and effectiveness of neuroevolution in complex real-time strategy environments.

This paper investigates the application of Neuroevolution of Augmenting Topologies (NEAT) to automate gameplay in Dark Souls, a notoriously challenging action role-playing game characterized by complex combat mechanics, dynamic environments, and high-dimensional visual inputs. Unlike traditional reinforcement learning or game playing approaches, our method evolves neural networks directly from raw pixel data, circumventing the need for explicit game-state information. To facilitate this approach, we introduce the Dark Souls API (DSAPI), a novel Python framework leveraging real-time computer vision techniques for extracting critical game metrics, including player and enemy health states. Using NEAT, agents evolve effective combat strategies for defeating the Asylum Demon, the game's initial boss, without predefined behaviors or domain-specific heuristics. Experimental results demonstrate that evolved agents achieve up to a 35% success rate, indicating the viability of neuroevolution in addressing complex, visually intricate gameplay scenarios. This work represents an interesting application of vision-based neuroevolution, highlighting its potential use in a wide range of challenging game environments lacking direct API support or well-defined state representations.

This paper introduces a novel approach to evolving a shogi evaluation function using genetic algorithms. This study explores an alternative to the commonly used mentor-assisted learning methods for shogi, also known as Japanese chess. Instead of relying on established concepts for learning evaluation functions, such as mentor-assisted learning, we employ a genetic algorithm utilizing the winning player as the sole learning input. Our novel dataset, compiled from 1 million board states scraped from professional games, served as the foundation for training. Our approach yielded a 70 % classification accuracy in determining the winner of shogi games from previously unseen board states when tested on a validation set. The results highlight the effectiveness of using genetic algorithms to evolve a shogi evaluation function and provide a further understanding of enhancing shogi AI using game outcomes as primary training data. Our method's computational efficiency also stands as an advantage over other techniques commonly employed in this domain. This work offers a fresh perspective in the realm of computer shogi with implications for future research and development.

In this research, we evolve gaits for an arachnid-inspired robot. The method used is an expansion upon previous research on the incremental evolution of gaits for hexapod robots with two degrees of freedom per leg, which we now apply to a more complex, eight-legged robot with three degrees of freedom per leg. Incremental evolution handles gait generation for legged robots in two discrete increments. The first increment uses a cyclic genetic algorithm to learn the activations (pulse instructions to the servos) required for each leg to perform a single-leg cycle. This learning program takes into account the way each leg is mounted on the body and the range of movement provided by the three servos on each leg to produce a smooth, straight, and efficient leg cycle. The second increment uses a genetic algorithm to select the best combination of leg cycles for each leg and to learn the timing to execute each leg cycle to coordinate them all together into a single gait. In this work, we learn the gait incrementally in a simulation and transfer the final gaits to the real robot to confirm the method's viability.

This article consists of a collection of slides from the author's conference presentation on the special features, system design and architectures, processing capabilities, and targeted markets for CoinTerra's Goldstrike, a first generation crypto-currency processor for Bitcoin mining machines.

Anytime Learning with Fitness Biasing has been shown in previous works to be an effective tool for evolving hexapod gaits. In this paper, we present the use of Anytime Learning with Fitness Biasing to evolve the controller for a robot learning the box pushing task. The robot that was built for this task, was measured to create an accurate model. The model was used in simulation to test the effectiveness of Anytime Learning with Fitness Biasing for the box pushing task. This work is the first step in new research where an automated system to test the viability of Fitness Biasing will be created, as well as the first application of Fitness Biasing to a high level task such as box pushing.

The communication system is currently undergoing a convergence to IP services. As a result, voice over internet protocol (VoIP) will be commonplace in the near future. In order to maximize the voice capacity, the overhead associated with controlling VoIP transmissions must be carefully managed. The current efforts in B3G (beyond 3G) standards development to efficiently control VoIP transmissions by grouping VoIP users into scheduling groups, assigning the group a set of shared time-frequency resources, and using bitmap signaling to allocate resources were detailed in [1]. This paper introduces several improvement mechanisms which enhance the basic concept outlined in [1]. System level simulations are used to validate the improved signaling technique and show that the technique can efficiently support 133 VoIP users per megahertz in a VoIP only system and 64 VoIP users per megahertz plus 1.05 Mbps for the traffic mixed considered in a mixed VoIP/data system.

A cross-layer algorithm for geographic routing in wireless sensor networks (WSNs) is proposed, which is robust to dead-ends and resilient to topological variations due to network dynamics. The solution combines ideas of network tessellation (clusterization) with greedy forwarding, without suffering from the problems afflicting landmark-based alternatives. The clusterization algorithm is based on a discovered graph-spectral property and relies on connectivity information only. Cluster sizes can be varied, allowing for different trade-offs between packet delivery success ratio (PDSR) and average packet delivery latency (APDL) to be reached. Simulation results show that the technique can substantially improve the PDSR in networks where large concave holes (dead-ends) are present, with no or little impact on APDL.

Revision B of the high rate packet data standard defines several new transmission formats for forward traffic channel transmission. The new transmission formats provide higher order modulation, an increased quantization in nominal data rate, and more opportunity for hybrid automatic repeat request gain. Consequently, the access terminal has many more choices when mapping a measured pilot signal-to-noise ratio to a requested transmission format. Ultimately, the new transmission formats result in a more efficient use of system resources leading to increased system performance. In this paper, we show that system level performance is increased by 25% for one receive antenna and 28% for two receive antennas as a result of the new transmission formats. We further show the distribution of selected transmission formats.